Incremental query evaluation for support vector machines

Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches. In such a SVM active learning environment, the system first processes k sampling queries and top-k uncertain queries to select the candidate data items for training. The user’s top-k relevant queries are then evaluated to compute the answer. This approach has shown to be effective. However, it suffers from the scalability problem associated with larger database sizes. To address this limitation, we propose an incremental query evaluation technique for these three types of queries. Based on the observation that most queries are not revised dramatically during the iterative evaluation, the proposed technique reuses the results of previous queries to reduce the computation cost. Furthermore, this technique takes advantage of a tuned index structure to efficiently prune irrelevant data. As a result, only a small portion of the data set needs to be access...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Danzhou Liu, Kien A. Hua
• Keywords: CIKM 2009, Database, Multimedia Retrieval, Support Vector Machines, Top-k Uncertain Queries